feat: add xDiT support fro parallel inference

README.md

@@ -358,6 +358,9 @@ This folder contains some tools for model conversion / caption generation, etc.
   Adapter.
 + [llm_flux_cogvideox](tools/llm_flux_cogvideox/llm_flux_cogvideox.py): Automatically generate videos using an
   open-source local large language model + Flux + CogVideoX.
++ [parallel_inference_xdit](tools/parallel_inference/parallel_inference_xdit.py):
+Supported by [xDiT](https://github.com/xdit-project/xDiT), parallelize the
+  video generation process on multiple GPUs.
 
 ## CogVideo(ICLR'23)
 

README_ja.md

@@ -329,6 +329,9 @@ pipe.vae.enable_tiling()
   をロードするためのツールコード。
 + [llm_flux_cogvideox](tools/llm_flux_cogvideox/llm_flux_cogvideox.py): オープンソースのローカル大規模言語モデル +
   Flux + CogVideoX を使用して自動的に動画を生成します。
++ [parallel_inference_xdit](tools/parallel_inference/parallel_inference_xdit.py)：
+[xDiT](https://github.com/xdit-project/xDiT)
+  によってサポートされ、ビデオ生成プロセスを複数の GPU で並列化します。
 
 ## CogVideo(ICLR'23)
 

README_zh.md

@@ -312,6 +312,9 @@ pipe.vae.enable_tiling()
 + [load_cogvideox_lora](tools/load_cogvideox_lora.py): 载入diffusers版微调Lora Adapter的工具代码。
 + [llm_flux_cogvideox](tools/llm_flux_cogvideox/llm_flux_cogvideox.py): 使用开源本地大语言模型 + Flux +
   CogVideoX实现自动化生成视频。
++ [parallel_inference_xdit](tools/parallel_inference/parallel_inference_xdit.py):
+在多个 GPU 上并行化视频生成过程，
+  由[xDiT](https://github.com/xdit-project/xDiT)提供支持。
 
 ## CogVideo(ICLR'23)
 

tools/parallel_inference/parallel_inference_xdit.py

@@ -0,0 +1,101 @@
+"""
+This is a parallel inference script for CogVideo. The original script
+can be found from the xDiT project at
+
+https://github.com/xdit-project/xDiT/blob/main/examples/cogvideox_example.py
+
+By using this code, the inference process is parallelized on multiple GPUs,
+and thus speeded up.
+
+You can also use the run.sh file in the same folder to automate running this
+code for batch generation of videos.
+
+Usage:
+1. pip install xfuser
+2. run the following command to generate video
+torchrun --nproc_per_node=4 cogvideox_xdit.py --model <cogvideox-model-path> \
+    --ring_degree 2 --use_cfg_parallel --height 480 --width 720 --num_frames 9 \
+    --prompt 'A small dog.'
+
+"""
+
+import time
+import torch
+import torch.distributed
+from diffusers import AutoencoderKLTemporalDecoder
+from xfuser import xFuserCogVideoXPipeline, xFuserArgs
+from xfuser.config import FlexibleArgumentParser
+from xfuser.core.distributed import (
+    get_world_group,
+    get_data_parallel_rank,
+    get_data_parallel_world_size,
+    get_runtime_state,
+    is_dp_last_group,
+)
+from diffusers.utils import export_to_video
+
+
+def main():
+    parser = FlexibleArgumentParser(description="xFuser Arguments")
+    args = xFuserArgs.add_cli_args(parser).parse_args()
+    engine_args = xFuserArgs.from_cli_args(args)
+
+    # Check if ulysses_degree is valid
+    num_heads = 30
+    if engine_args.ulysses_degree > 0 and num_heads % engine_args.ulysses_degree != 0:
+        raise ValueError(
+            f"ulysses_degree ({engine_args.ulysses_degree}) must be a divisor of the number of heads ({num_heads})"
+        )
+
+    engine_config, input_config = engine_args.create_config()
+    local_rank = get_world_group().local_rank
+
+    pipe = xFuserCogVideoXPipeline.from_pretrained(
+        pretrained_model_name_or_path=engine_config.model_config.model,
+        engine_config=engine_config,
+        torch_dtype=torch.bfloat16,
+    )
+    if args.enable_sequential_cpu_offload:
+        pipe.enable_model_cpu_offload(gpu_id=local_rank)
+        pipe.vae.enable_tiling()
+    else:
+        device = torch.device(f"cuda:{local_rank}")
+        pipe = pipe.to(device)
+
+    torch.cuda.reset_peak_memory_stats()
+    start_time = time.time()
+
+    output = pipe(
+        height=input_config.height,
+        width=input_config.width,
+        num_frames=input_config.num_frames,
+        prompt=input_config.prompt,
+        num_inference_steps=input_config.num_inference_steps,
+        generator=torch.Generator(device="cuda").manual_seed(input_config.seed),
+        guidance_scale=6,
+    ).frames[0]
+
+    end_time = time.time()
+    elapsed_time = end_time - start_time
+    peak_memory = torch.cuda.max_memory_allocated(device=f"cuda:{local_rank}")
+
+    parallel_info = (
+        f"dp{engine_args.data_parallel_degree}_cfg{engine_config.parallel_config.cfg_degree}_"
+        f"ulysses{engine_args.ulysses_degree}_ring{engine_args.ring_degree}_"
+        f"tp{engine_args.tensor_parallel_degree}_"
+        f"pp{engine_args.pipefusion_parallel_degree}_patch{engine_args.num_pipeline_patch}"
+    )
+    if is_dp_last_group():
+        world_size = get_data_parallel_world_size()
+        resolution = f"{input_config.width}x{input_config.height}"
+        output_filename = f"results/cogvideox_{parallel_info}_{resolution}.mp4"
+        export_to_video(output, output_filename, fps=8)
+        print(f"output saved to {output_filename}")
+
+    if get_world_group().rank == get_world_group().world_size - 1:
+        print(f"epoch time: {elapsed_time:.2f} sec, memory: {peak_memory/1e9} GB")
+    get_runtime_state().destory_distributed_env()
+
+
+if __name__ == "__main__":
+    main()

tools/parallel_inference/run.sh

@@ -0,0 +1,51 @@
+set -x
+
+export PYTHONPATH=$PWD:$PYTHONPATH
+
+# Select the model type
+# The model is downloaded to a specified location on disk, 
+# or you can simply use the model's ID on Hugging Face, 
+# which will then be downloaded to the default cache path on Hugging Face.
+
+export MODEL_TYPE="CogVideoX"
+# Configuration for different model types
+# script, model_id, inference_step
+declare -A MODEL_CONFIGS=(
+    ["CogVideoX"]="parallel_inference_xdit.py /cfs/dit/CogVideoX-2b 20"
+)
+
+if [[ -v MODEL_CONFIGS[$MODEL_TYPE] ]]; then
+    IFS=' ' read -r SCRIPT MODEL_ID INFERENCE_STEP <<< "${MODEL_CONFIGS[$MODEL_TYPE]}"
+    export SCRIPT MODEL_ID INFERENCE_STEP
+else
+    echo "Invalid MODEL_TYPE: $MODEL_TYPE"
+    exit 1
+fi
+
+mkdir -p ./results
+
+# task args
+if [ "$MODEL_TYPE" = "CogVideoX" ]; then
+  TASK_ARGS="--height 480 --width 720 --num_frames 9"
+fi
+
+# CogVideoX asserts sp_degree == ulysses_degree*ring_degree <= 2. Also, do not set the pipefusion degree.
+if [ "$MODEL_TYPE" = "CogVideoX" ]; then
+N_GPUS=4
+PARALLEL_ARGS="--ulysses_degree 2 --ring_degree 1"
+CFG_ARGS="--use_cfg_parallel"
+fi
+
+
+torchrun --nproc_per_node=$N_GPUS ./$SCRIPT \
+--model $MODEL_ID \
+$PARALLEL_ARGS \
+$TASK_ARGS \
+$PIPEFUSION_ARGS \
+$OUTPUT_ARGS \
+--num_inference_steps $INFERENCE_STEP \
+--warmup_steps 0 \
+--prompt "A small dog." \
+$CFG_ARGS \
+$PARALLLEL_VAE \
+$COMPILE_FLAG